






"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)




class 						__UpperCamelCase     (     unittest.TestCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	Union[str, Any]=13     , UpperCAmelCase							:	str=7     , UpperCAmelCase							:	int=True     , UpperCAmelCase							:	Tuple=True     , UpperCAmelCase							:	List[Any]=True     , UpperCAmelCase							:	Dict=True     , UpperCAmelCase							:	Tuple=99     , UpperCAmelCase							:	Optional[Any]=32     , UpperCAmelCase							:	str=5     , UpperCAmelCase							:	str=4     , UpperCAmelCase							:	int=37     , UpperCAmelCase							:	List[Any]="gelu"     , UpperCAmelCase							:	str=0.1     , UpperCAmelCase							:	Optional[int]=0.1     , UpperCAmelCase							:	Tuple=512     , UpperCAmelCase							:	int=16     , UpperCAmelCase							:	int=2     , UpperCAmelCase							:	List[str]=0.0_2     , UpperCAmelCase							:	List[Any]=4     , )	->       Union[str, Any]:
											lowerCAmelCase   :str						=			parent
											lowerCAmelCase   :str						=			batch_size
											lowerCAmelCase   :Union[str, Any]						=			seq_length
											lowerCAmelCase   :int						=			is_training
											lowerCAmelCase   :str						=			use_attention_mask
											lowerCAmelCase   :Union[str, Any]						=			use_token_type_ids
											lowerCAmelCase   :int						=			use_labels
											lowerCAmelCase   :List[str]						=			vocab_size
											lowerCAmelCase   :Dict						=			hidden_size
											lowerCAmelCase   :Optional[Any]						=			num_hidden_layers
											lowerCAmelCase   :List[Any]						=			num_attention_heads
											lowerCAmelCase   :Optional[int]						=			intermediate_size
											lowerCAmelCase   :Union[str, Any]						=			hidden_act
											lowerCAmelCase   :List[str]						=			hidden_dropout_prob
											lowerCAmelCase   :int						=			attention_probs_dropout_prob
											lowerCAmelCase   :str						=			max_position_embeddings
											lowerCAmelCase   :Tuple						=			type_vocab_size
											lowerCAmelCase   :str						=			type_sequence_label_size
											lowerCAmelCase   :int						=			initializer_range
											lowerCAmelCase   :Dict						=			num_choices
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[Any]:
											lowerCAmelCase   :int						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											lowerCAmelCase   :Any						=			None
											if self.use_attention_mask:
																	lowerCAmelCase   :Dict						=			random_attention_mask([self.batch_size, self.seq_length]   )

											lowerCAmelCase   :Optional[int]						=			None
											if self.use_token_type_ids:
																	lowerCAmelCase   :List[Any]						=			ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size   )

											lowerCAmelCase   :Union[str, Any]						=			RoFormerConfig(
											    vocab_size=self.vocab_size     , hidden_size=self.hidden_size     , num_hidden_layers=self.num_hidden_layers     , num_attention_heads=self.num_attention_heads     , intermediate_size=self.intermediate_size     , hidden_act=self.hidden_act     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , max_position_embeddings=self.max_position_embeddings     , type_vocab_size=self.type_vocab_size     , is_decoder=UpperCAmelCase     , initializer_range=self.initializer_range     , )

											return config, input_ids, token_type_ids, attention_mask
					def        UpperCAmelCase__ (							self							:	str   )	->       int:
											lowerCAmelCase   :Dict						=			self.prepare_config_and_inputs()
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			config_and_inputs
											lowerCAmelCase   :List[Any]						=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict




@require_flax
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Dict      =  True

					lowercase_    :				Optional[Any]      =  (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					def        UpperCAmelCase__ (							self							:	Any   )	->       List[Any]:
											lowerCAmelCase   :Optional[int]						=			FlaxRoFormerModelTester(self   )
					@slow
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[Any]:
											for model_class_name in self.all_model_classes:
																	lowerCAmelCase   :Tuple						=			model_class_name.from_pretrained('junnyu/roformer_chinese_small'     , from_pt=UpperCAmelCase   )
																	lowerCAmelCase   :Any						=			model(np.ones((1, 1)   )   )
																	self.assertIsNotNone(UpperCAmelCase   )




@require_flax
class 						__UpperCamelCase     (     unittest.TestCase						):
					@slow
					def        UpperCAmelCase__ (							self							:	int   )	->       Tuple:
											lowerCAmelCase   :Any						=			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'   )
											lowerCAmelCase   :Union[str, Any]						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
											lowerCAmelCase   :Optional[Any]						=			model(UpperCAmelCase   )[0]

											lowerCAmelCase   :int						=			5_0000

											lowerCAmelCase   :Dict						=			(1, 6, vocab_size)
											self.assertEqual(output.shape     , UpperCAmelCase   )

											lowerCAmelCase   :Any						=			jnp.array(
											    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]   )

											self.assertTrue(jnp.allclose(output[:, :3, :3]     , UpperCAmelCase     , atol=1e-4   )   )







"""simple docstring"""


from math import sqrt


def        UpperCAmelCase      (					a__ = 1_00_00_00 ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int

						while num_cuboids <= limit:
												max_cuboid_size += 1
												for sum_shortest_sides in range(2     ,      2 * max_cuboid_size + 1 ):
																		if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
																								num_cuboids += (
																								    min(a__     ,      sum_shortest_sides // 2 )
																								    - max(1     ,      sum_shortest_sides - max_cuboid_size )
																								    + 1
																								)

						return max_cuboid_size


if __name__ == "__main__":
		print(F"""{solution() = }""")